Spaces:
Configuration error
Configuration error
File size: 2,471 Bytes
a23872f eac223c a23872f e0f92a0 a23872f 0be9cd5 a23872f 0be9cd5 a23872f 4e7a12f a23872f eac223c a23872f e0f92a0 a23872f e0f92a0 a23872f e0f92a0 a23872f e0f92a0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 |
import importlib
import numpy as np
import taming
import torch
import yaml
from omegaconf import OmegaConf
from PIL import Image
from taming.models.vqgan import VQModel
from utils import get_device
def load_config(config_path, display=False):
config = OmegaConf.load(config_path)
if display:
print(yaml.dump(OmegaConf.to_container(config)))
return config
def load_default(device):
conf_path = "./model_checkpoints/vqgan_only.yaml"
config = load_config(conf_path, display=False)
model = taming.models.vqgan.VQModel(**config.model.params)
sd = torch.load("./model_checkpoints/vqgan_only.pt", map_location=device)
model.load_state_dict(sd, strict=True)
model.to(device)
del sd
return model
def load_vqgan(config, ckpt_path=None, is_gumbel=False):
model = VQModel(**config.model.params)
if ckpt_path is not None:
sd = torch.load(ckpt_path, map_location="cpu")["state_dict"]
missing, unexpected = model.load_state_dict(sd, strict=False)
return model.eval()
def reconstruct_with_vqgan(x, model):
z, _, [_, _, indices] = model.encode(x)
print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
xrec = model.decode(z)
return xrec
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def instantiate_from_config(config):
if "target" not in config:
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
def load_model_from_config(config, sd, gpu=True, eval_mode=True):
model = instantiate_from_config(config)
if sd is not None:
model.load_state_dict(sd)
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def load_model(config, ckpt, gpu, eval_mode):
# load the specified checkpoint
if ckpt:
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
print(f"loaded model from global step {global_step}.")
else:
pl_sd = {"state_dict": None}
global_step = None
model = load_model_from_config(
config.model, pl_sd["state_dict"], gpu=gpu, eval_mode=eval_mode
)["model"]
return model, global_step
|